polars-luxical 0.1.1

A Polars plugin for fast lexical text embeddings

Polars Luxical

A high-performance Polars plugin for Luxical text embeddings, implemented in Rust.

Overview

This plugin provides Luxical embeddings directly within Polars expressions. Luxical combines:

• Subword tokenization (BERT uncased)
• N-gram feature extraction with TF-IDF weighting
• Sparse-to-dense neural network projection via knowledge distillation

Luxical models achieve dramatically higher throughput than transformer-based embedding models while maintaining competitive quality for document-level similarity tasks like clustering, classification, and semantic deduplication.

It should be noted that they were not trained on queries, so you cannot use them for search! A demonstration of this is given in the benchmarks, where the results are fast but not useful.

Installation

pip install polars-luxical

Or build from source:

maturin develop --release

Model Download

Models are automatically downloaded from HuggingFace Hub and cached locally on first use.

Cache locations:

• Linux: ~/.cache/polars-luxical/
• macOS: ~/Library/Caches/polars-luxical/
• Windows: C:\Users\<User>\AppData\Local\polars-luxical\

To use a local model file instead:

register_model("/path/to/your/model")

Both .safetensors and .npz formats are supported.

Usage

import polars as pl
from polars_luxical import register_model, embed_text

# Register a Luxical model (downloads and caches automatically)
register_model("DatologyAI/luxical-one")

# Create a DataFrame
df = pl.DataFrame({
    "id": [1, 2, 3],
    "text": [
        "Hello world",
        "Machine learning is fascinating",
        "Polars and Rust are fast",
    ],
})

# Embed text
df_emb = df.with_columns(
    embed_text("text", model_id="DatologyAI/luxical-one").alias("embedding")
)
print(df_emb)

# Or use the namespace API
df_emb = df.luxical.embed(
    columns="text",
    model_name="DatologyAI/luxical-one",
    output_column="embedding",
)

# Retrieve similar documents
results = df_emb.luxical.retrieve(
    query="Tell me about speed",
    model_name="DatologyAI/luxical-one",
    embedding_column="embedding",
    k=3,
)
print(results)

Available Models

Model ID	Description	Embedding Dim
DatologyAI/luxical-one	English web documents, distilled from snowflake-arctic-embed-m-v2.0	192

Performance

Luxical embeddings avoid transformer inference entirely, achieving throughput up to ~100x faster than large transformer embedding models (e.g., Qwen3-0.6B) and significantly faster than smaller models like MiniLM-L6-v2, particularly on CPU.

For benchmarks and methodology, see the Luxical technical report.

API Reference

Functions

register_model(model_name: str, providers: list[str] | None = None) -> None

Register/load a Luxical model into the global registry. If already loaded, this is a no-op.

• model_name: HuggingFace model ID (e.g., "DatologyAI/luxical-one") or local path.
• providers: Ignored (kept for API compatibility).

embed_text(expr, *, model_id: str | None = None) -> pl.Expr

Embed text using a Luxical model.

• expr: Column expression containing text to embed.
• model_id: Model name/ID. If None, uses the default model.

clear_registry() -> None

Clear all loaded models from the registry (frees memory).

list_models() -> list[str]

Return a list of currently loaded model names.

DataFrame Namespace

df.luxical.embed(columns, model_name, output_column="embedding", join_columns=True)

Embed text from specified columns.

df.luxical.retrieve(query, model_name, embedding_column="embedding", k=None, threshold=None, similarity_metric="cosine", add_similarity_column=True)

Retrieve rows most similar to a query.

License

Apache 2.0